The cross-project defect prediction based on PSO and Feature Dependent Naive Bayes

Abstract

The software defect prediction often assumes that the software under test has rich historical information, which is a harsh condition in reality. Cross-project defect prediction can predict projects with rare historical information by using historically informative projects. The differences in software architecture and code environment pose challenges to cross-project software defect prediction. Based on the characteristics of software defect prediction sets, this paper introduces a two-stage algorithm based on particle swarm optimization algorithm and Feature Dependent Naive Bayesian classifier.